Hypothesis testing has been used for many decades to develop and prove or disprove a statistical inference between variables. These tests help us to either reject the null hypothesis or accept the alternative hypothesis.
Statistical inferences are simply that: inferences. They cannot be regarded as causal in any way. In order to prove that one independent variable causes a dependent variable to perform in a specific way, such as antibiotics kill bacterial viruses, requires multiple blind studies and additional proof. However, these tests can, and often do, provide insights as to why certain processes behave or relate to other processes in a particular way. For example, is there a relationship between the age of your vehicle and visits to the mechanic?
Imbedded into hypothesis testing is the possibility of a Type I or Type II error. A Type I error is the possibility of incorrectly rejecting the true null hypothesis or a false positive. For example, a Type I error might indicate a relationship between the sale of vanilla wafers and bananas (banana pudding) when there actually is no relationship at all. Customers are not buying the two together. As you can see, such an error could prove costly to stores and vendors.
A Type II error is failing to reject a false hypothesis. A Type II error is the most critical to occur. For example, a medical test fails to detect cancer when the patient actually has cancer. A diagnostic engine test fails to detect a faulty engine when the engine really does have a major internal flaw. A Type II error can be catastrophic, costly, and even deadly when human subjects are involved.
The hypothesis test in research is of utmost importance. Hypothesis testing helps us to solve problems and to make sound decisions – important skills for business professionals.
If we are to be successful in business, we must understand the importance of hypothesis testing in research and its intended applications. Before you begin this assignment, David Longstreet’s (2011) video on Hypothesis Testing for Type I and Type II Errors will prove helpful.
The article should be empirical in nature and on a topic in business or management that interests you. After reading and analyzing the article, consider the hypothesis proposed in the study article and address the topics below:
Identify and describe the purpose of the study.
Identify and describe the hypothesis for the study.
What statistical analysis was used in testing the hypothesis?
Was the hypothesis proved or disproved?
Would you have changed the hypothesis or statistical test used in the study? Why?
Identify the difference between a Type I and Type II error. Did the article discuss the potential for these type of errors in the analysis?
Cite your source(s) in APA format.
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